update

Author: sivasathyaseeelan

Project.toml

@@ -12,7 +12,6 @@ DocStringExtensions = "ffbed154-4ef7-542d-bbb7-c09d3a79fcae"
 FunctionWrappers = "069b7b12-0de2-55c6-9aab-29f3d0a68a2e"
 Graphs = "86223c79-3864-5bf0-83f7-82e725a168b6"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
-Markdown = "d6f4376e-aef5-505a-96c1-9c027394607a"
 PoissonRandom = "e409e4f3-bfea-5376-8464-e040bb5c01ab"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 RecursiveArrayTools = "731186ca-8d62-57ce-b412-fbd966d074cd"

src/simple_regular_solve.jl

@@ -137,7 +137,7 @@ function DiffEqBase.solve(jump_prob::JumpProblem, alg::SimpleAdaptiveTauLeaping;
         hor[j] = sum(abs.(nu[:, j])) > maximum(abs.(nu[:, j])) ? 2 : 1
     end
 
-    saveat_times = isnothing(saveat) ? Float64[] : saveat isa Number ? collect(range(tspan[1], tspan[2], step=saveat)) : collect(saveat)
+    saveat_times = isnothing(saveat) ? Vector{typeof(t)}() : saveat isa Number ? collect(range(tspan[1], tspan[2], step=saveat)) : collect(saveat)
     save_idx = 1
 
     while t[end] < t_end
@@ -161,7 +161,7 @@ function DiffEqBase.solve(jump_prob::JumpProblem, alg::SimpleAdaptiveTauLeaping;
         counts .= pois_rand.(rng, max.(rate_cache * tau, 0.0))
         c(du, u_prev, p, t_prev, counts, nothing)
         u_new = u_prev + du
-        if any(u_new .< 0)
+        if any(<(0), u_new)
             # Halve tau to avoid negative populations, as per Cao et al. (2006, J. Chem. Phys., DOI: 10.1063/1.2159468)
             tau /= 2
             continue